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TAG BASED SEARCH AND RECOMMENDATION IN SOCIAL MEDIA
by
Sang Su Lee
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
December 2011
Copyright 2011 Sang Su Lee

Social media, unlike traditional media, facilitate direct and real-time interaction among users. The increase in interaction results in massive amounts of data, which require appropriate categorization so that users can find the specific information they need. Traditional categorization by a few moderators cannot handle the massive amount of information being generated. Instead, tags, which are free-format keywords or terms created by users to describe content, are best able to categorize information in social media and to cope with the large amount of data in a timely manner. Each tag may not perfectly describe the content, but the aggregated tags reflect the knowledge of multiple users and result in a taxonomy for categorization. ❧ This categorization method can locate appropriate information for users, via search and recommendation functions. Search functions allow users to locate appropriate information from within the entire dataset, and recommendation functions involve the system actively suggesting appropriate information for the user. The performance of search and recommendation functions are degraded by the ambiguity inherent in the free format of tagging. To resolve this ambiguity and to propose better search and recommendation results, extra information, such as time and location, should be used. In this thesis, we describe how tags and extra information can be used for search and recommendation functions. ❧ We present an analysis of the correlation between tags and geographical identification metadata, or geotags. To make the analysis of geotagging and tagging possible, we prove that there is a strong correlation between these two types of information. Our approach uses similarity between tags and geographical distribution to determine interrelationships between tags and geotags. From our initial experiments, we show that the power law is established between tag similarity and geographical distribution similarity. We also present a system for recommendations that uses a modified latent Dirichlet allocation model in which users and tags associated with an item are represented and clustered by topics, and the topic-based representation is combined with the item’s time stamp to show time-based topic distribution. By representing users via topics, the model can cluster users to reveal common interests. Based on this model, we developed a recommendation system that reflects both user and group interests in a dynamic manner that accounts for time. ❧ This thesis contributes to the understanding of the use of tagging and improves the use of tagging in social media. In addition, this thesis provides guidance on user behavior analysis in social media.

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TAG BASED SEARCH AND RECOMMENDATION IN SOCIAL MEDIA
by
Sang Su Lee
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
December 2011
Copyright 2011 Sang Su Lee